🤖 AI Summary
This work addresses the high computational overhead of conventional neural networks that hinders real-time EEG classification on edge devices. It introduces, for the first time, differentiable logic gate networks (Diff-Logic) to EEG classification, compiling them into pure Boolean circuits and leveraging native CPU bitwise operations for hardware-efficient inference. The proposed approach matches or surpasses the performance of multilayer perceptrons (MLPs) while drastically reducing latency and model size. In dementia screening, it achieves a Macro F1 score of 80.2%, outperforming MLPs by 6.8%. For emotion recognition, it reduces model size by 14× and latency by 2.3×, with up to a 2.9× speedup in inference at the largest scale and near-constant inference time—making it highly suitable for resource-constrained edge deployment.
📝 Abstract
Real-time EEG classification on edge devices is bottlenecked by the floating-point arithmetic of conventional neural networks. We investigated Differentiable Logic Gate Networks (Diff-Logic) as a hardware-native alternative that compiles models into pure Boolean circuits executable via bitwise CPU operations. Through rigorous iso-parameter experiments across four EEG datasets spanning two classification tasks, binary dementia detection and 3-class emotion recognition, we compared Diff-Logic against matched-capacity Multi-Layer Perceptron (MLP) and Binarized Neural Network (BNN) baselines at four complexity tiers (50k-500k parameters). On dementia screening, Diff-Logic achieved 80.2% Macro F1, outperforming the MLP baseline by 6.8%. On emotion recognition, the MLP retained a moderate performance advantage but incurred a 2.3$\times$ higher latency and 14$\times$ larger model size when deployed on a power-constrained (7W) Nvidia Jetson Orin Nano CPU (Single-core). Critically, Diff-Logic inference time remained nearly constant across a 10$\times$ increase in model scale, achieving a peak speedup of 2.9$\times$ over MLPs at the largest complexity tier. Our results establish logic-based neural architectures as a practical paradigm for resource-constrained brain-computer interfaces, achieving competitive or superior performance while natively satisfying the latency and memory constraints of portable edge deployment. Code is available on GitHub: https://github.com/Shyamal-Dharia/eeg-difflogic